AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Nevus

Showing 1 to 10 of 26 articles

Clear Filters

Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)-based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger...

A hybrid CNN with transfer learning for skin cancer disease detection.

Medical & biological engineering & computing
The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the precise classification of skin lesions. However, dermatologists may find it difficult and time-consuming ...

Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection.

The Journal of investigative dermatology
Melanoma is still a major health problem worldwide. Early diagnosis is the first step toward reducing its mortality, but it remains a challenge even for experienced dermatologists. Although computer-aided systems have been developed to help diagnosis...

Enhancing Choroidal Nevus Position Identification through CNN-Based Segmentation of Eye Fundus Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effective in detecting and analyzing such abnormalities with high accuracy and efficiencyThis research is pa...

Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks.

Journal of X-ray science and technology
BACKGROUND: With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in th...

Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population.

International journal of environmental research and public health
(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma i...

Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features.

Computational and mathematical methods in medicine
The objective of this study was to explore the image classification and case characteristics of pigmented nevus (PN) diagnosed by dermoscopy under deep learning. 268 patients were included as the research objects and they were randomly divided into o...

Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.

Dermatology (Basel, Switzerland)
BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuab...

Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Journal of cutaneous pathology
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...